In this work, we propose an edit-based output sequence
representation (shown in Figure REF ): [Edit Plan] <s> [Target Sequence], where the
model is trained to generate an edit plan ([baseline=(char.base)]
shape=circle, draw=black, minimum size=0.1, inner sep=0.8pt, fill=white, thick, text=black] (char) 1;) consisting of
explicit edit operations that reconstruct the input sequence, followed by a separation token
(<s>), and finally the target sequence ([baseline=(char.base)]
shape=circle, draw=black, minimum size=0.1, inner sep=0.8pt, fill=white, thick, text=black] (char) 2;) that matches
the original input sequence. This is inspired by the concept of
content planning, originating from natural language
generation [1]}. In content planning, a
high-level plan is first outlined, specifying the discourse structure
of the content to be generated, and then lexical realization is
performed to generate the text.
